Weekly Materials
Lecture slides, week 8
Professor's lecture slides (PDF)
Discussion of the previous exercises; Finger exercises (with TA)
Hands-on tutorials and practice exercises
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Exercise 5
Distribution of Exercise sheet 5
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References & Resources
Finger Exercises
Discussion of the previous exercises; Finger exercises (with TA)
Additional Notes
Week 8: TensorFlow/PyTorch
Learning Objectives
- Master popular deep learning frameworks: TensorFlow and PyTorch
- Understand sequence modeling with RNNs and LSTMs
- Apply deep learning to time series and sequential data
- Develop practical skills in framework-specific implementations
- Explore self-attention mechanisms and their applications
Topics Covered
- TensorFlow Tour: Comprehensive introduction to TensorFlow ecosystem
- PyTorch Introduction: Practical PyTorch for machine learning applications
- Deep Learning for Sequence Modeling: Working with sequential data
- Recurrent Neural Networks (RNNs): Understanding sequential dependencies
- Long Short-Term Memory (LSTMs): Advanced sequence modeling
- Self-Attention: Introduction to attention mechanisms
Schedule
- Lecture: Monday, November 3, 2025 (10:15 - 12:00)
- Practice Session: Monday, November 3, 2025 (16:30 - 18:00)
- TA Session: Framework comparison and hands-on exercises
Key Concepts
- Framework Comparison: TensorFlow vs PyTorch advantages and use cases
- Computational Graphs: Static vs dynamic graph construction
- Sequential Data: Time series, text, and other sequential patterns
- RNN Architecture: Vanilla RNNs, vanishing gradient problem
- LSTM Components: Forget gate, input gate, output gate
- Attention Mechanisms: Self-attention and transformer basics
Practical Applications
- Function Approximation: Using TensorFlow for analytical functions
- Time Series Prediction: LSTM models for ozone concentration data
- Stock Price Modeling: Financial time series analysis
- Sequence Classification: Text and sequential data classification
Hands-on Examples
- Warm-up with TensorFlow: Approximate analytical functions
- Comprehensive TensorFlow tour and best practices
- PyTorch introduction for supervised learning problems
- LSTM applications to real-world time series data
- Comparison of framework implementations
Assignments
- Exercise 5: Distributed this week - Framework implementation comparison
- Practice with both TensorFlow and PyTorch
- Implement RNN/LSTM models for sequence prediction
Technical Skills
- TensorFlow model building and training
- PyTorch tensor operations and automatic differentiation
- Sequence preprocessing and data preparation
- RNN/LSTM architecture design and optimization
- Model evaluation for sequential data